In this seminar, you will learn how ArcGIS Workflow Manager helps you organize, centralize, and standardize workflows in a flexible and distributed environment.This seminar was developed to support the following:ArcGIS Workflow Manager for Desktop

In this seminar, you will learn how to use ArcGIS Workflow Manager to organize, centralize, and manage your GIS operations and integrate them with your non-GIS workflows in a flexible and distributed environment.This seminar was developed to support the following:ArcGIS Desktop 10.2 (Standard Or Advanced)ArcGIS Workflow Manager for Desktop

The Workflow Manager JavaScript Application is designed to work with Esri Workflow Manager. This is a Javascript web application that connects to a Workflow Manager web service and allows users to access the workflows without needing the Esri Workflow Manager desktop application. There is a read me file associated with the application in the GitHub repository.

In this seminar, the presenters will introduce essential concepts of Collector for ArcGIS and show how this app integrates with other components of the ArcGIS platform to provide a seamless data management workflow. You will also learn how anyone in your organization can easily capture and update data in the field, right from their smartphone or tablet.This seminar was developed to support the following:ArcGIS Desktop 10.2.2 (Basic)ArcGIS OnlineCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Solutions Playbook for Mapping, Statistics, and Land Administration (MSL) consists of ArcGIS solutions for common workflows of national government accounts in the MSL sector.The contents of the playbook will be gradually completed and regularly updated by Esri Indonesia's Solution Strategist Team for National Government Sector.General Solutions:- Offline Mapping- Field Data Collection: Connecting ArcGIS Field Apps with External GNSS Receivers- Open DataNational Mapping and Charting:- Drone2Map for ArcGIS- Production Mapping- Product On DemandOfficial Statistics:- Workflow Manager- Data ReviewerAssessment, Tax, and Land Records:- Workflow Manager- Data Reviewer- Parcel FabricLast updated: Monday, 27 January 2020Copyright © 2020 Esri Indonesia. All rights reserved.

Descarga aquí el metadato:https://aplicaciones.siatac.co/geonetwork/srv/spa/catalog.search#/metadata/1742d666-50c8-4573-823e-5c5189ac0bbdDescarga aquí el shapefile:https://opendata.arcgis.com/datasets/31b4f21bfb6047659d5bc2b335d99eff_0.zipCorresponde a la capa de cicatrices por quemas en la Amazonía colombiana desde marzo del 2017 a escala 1:100.000. Para generar esta capa se seleccionan las imágenes satelitales, del programa LandSat; deben tener menos del 30% de nubes. Se hace una verificación de la cantidad puntos de calor detectados durante el mes de monitoreo, para corroborar cuales Path Row que cubren la región amazónica (4-57, 4-58, 4-59, 4-60, 4-61, 4-62, 4-63, 9-59, 9-60, 7-58, 7-59, 7-60, 7-61, 5-57, 5-58, 5-59, 5-60, 5-61, 5-62, 3-57, 3-58, 3-59, 8-58, 8-59, 8-60, 6-57, 6-58, 6-59, 6-60, 6-61, 6-62) deben priorizarse para la descarga.Para el procesamiento y clasificación de las imágenes, y los diferentes geoprocesos se usan herramientas del software ArcGis (Esri, 2022a). Con este programa se aplican los “Model Builder” que se han generado para este procesamiento, los cuales hacen parte de los flujos de trabajo (Workflow) construidos en la plataforma SIATAC. Con las imágenes se generan dos composiciones de color RGB , (1) una que integra el Índice de Vegetación de Diferencia Normalizada - NDVI (B5-B4/B5+B4), el Radio Normalizado de Quema-NBR (B5-B7/ (B5+B7) y la banda del infrarrojo cercano -IR (B5); (2) la otra composición se hace con las bandas B7-B5-B2; estas composiciones resaltan las áreas que han sufrido procesos de quema de la vegetación (Murcia & Otavo, 2018).Con la composición RGB (1) se hace una clasificación no supervisada tipo clúster (Clúster Iso) (Esri, 2022b) y se generan 11 clases. Sobre esta capa ráster se hace una verificación visual para determinar cuál de las 11 clases corresponde a las cicatrices, este proceso se hace con respaldo en el protocolo metodológico (Murcia et al., 2018) y las dos composiciones ya generadas. Una vez seleccionada la clase que se ha verificado como cicatrices, se hace una reclasificación binaria de las unidades, en la que uno (1) son cicatrices y cero (0) las otras clases. En el mismo proceso (Model Builder) se hace la vectorización y se genera la capa de polígonos de cicatrices.Luego se hace una verificación visual de los polígonos generados, para descartar aquellos que no son cicatrices, para esto se aplican los criterios previstos en el protocolo metodológico (Murcia et al., 2018) teniendo como referente las dos composiciones previamente generadas. Con la capa resultado se hace un proceso de análisis espacial de intersección (Esri, 2022c) para descartar las cicatrices que ya fueron clasificadas en el mes anterior.A la capa resultante se le hace control de calidad para verificar la exactitud temática, validando aspectos como delimitación, errores por omisión y errores por comisión. De igual modo, se verifica que la capa cumpla con todos los criterios de topología como la correcta adyacencia entre polígonos, y se aprueba la capa.En el siguiente paso, la capa aprobada se integra en un WorkFlow (Esri, 2022d) de la base de datos en la plataforma SIG de Esri, del SIATAC. Luego se aplica un proceso SIG de intersección mediante el cual se clasifican las cicatrices que se ubican en áreas que eran bosques, según la capa de bosques más reciente generada por el IDEAM (Ideam, 2022). Sobre los polígonos restantes, se aplica el mismo proceso SIG (intersección) con la capa de coberturas de la tierra, del periodo más reciente (Sinchi, 2022) y se clasifican las cicatrices que se ubican en donde había vegetación secundaria u otras coberturas, principalmente pastos.La capa resultante se somete a un proceso de análisis espacial de intersección para generar la información de las cicatrices con el tipo de cobertura vegetal afectada, por cada Unidad Espacial de Referencia (UER): Grandes paisajes, Jurisdicción de Corporaciones Autónomas Regionales o de Desarrollo sostenible, Estado legal del territorio, Departamentos y Municipios. Para finalizar, las estadísticas se publican en el portal del Sistema de Información Ambiental Territorial de la Amazonia colombiana -SIATAC (https://siatac.co/cicatrices-de-quema/).BIBLIOGRAFÍAMurcia, U. & Otavo, S. (2018). La amazonia se quema: Detección de áreas con mayor ocurrencia de incendios de vegetación como estrategia para la prevención y control. Revista Colombiana Amazónica No 11 de 2018, 59-72. https://sinchi.org.co/11-revista-colombia-amazonica.Cañon I., Gordillo G., León A., Murcia U., Romero H., Velásquez M. (2018). Protocolo para el monitoreo de cicatrices por quemas en la Amazonia colombiana. 46pp.Esri. (2022a). ArcGIS Desktop.https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview.Esri. (2022b). Clasificación no supervisada de clúster ISO.https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/iso-cluster-unsupervised-classification.htmEsri. (2022c). Intersección (Análisis).https://pro.arcgis.com/es/pro-app/latest/tool-reference/analysis/intersect.htmEsri. (2022d). ArcGIS Workflow Manager (Análisis).https://www.esri.com/en-us/arcgis/products/arcgis-workflow-manager/overviewIdeam. (2022). Sistema de Monitoreo de bosques y carbono SMBYC.https://smbyc.ideam.gov.co/MonitoreoBC-WEB/reg/indexLogOn.jspSinchi. (2022). Sistema de Monitoreo de las Coberturas de la tierra de la Amazonia colombiana SIMCOBA. Datos abiertos.https://datos.siatac.co/pages/coberturasDiccionario de datos:objectid: Corresponde al identificador propio de cada registro dentro de la capa de informaciónarea_ha: Corresponde al área en hectáreas de la unidad seleccionadaarea_km2: Corresponde al área en kilómetros cuadrados de la unidad seleccionadaano: Corresponde al año de publicación de la cicatriz de quemaorigen: Corresponde a la cobertura que fue afectada por la cicatriz de quemames: Corresponde al mes de publicación de la cicatriz de quemafecha_registro: Corresponde a la fecha de publicación de la cicatriz de quemashape: Corresponde a geometría del elementost_area(shape): Corresponde al área del elementost_length(shape): Corresponde al perímetro del elementoFuente:Modelos de Funcionamiento y Sostenibilidad del Laboratorio SIG y SRBogotá D.C., Colombia siatac.coCalle 20 # 5 - 44Código Postal: 110311 Teléfono: +57 (1) 4442060Horario de atención: 8:00 am - 5:00 pm de Lunes a Viernes Información de contacto:Establecer previo contacto telefónico o a través de correo electrónico, para realizar la solicitud o fijar una cita en el horario de atención.

The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

Land Management Software Market size was valued at USD 1.69 Billion in 2024 and is projected to reach USD 2.62 Billion by 2031, growing at a CAGR of 5.65% from 2024 to 2031.

The growth of land management software is primarily driven by the increasing demand for efficient land use, advancements in geospatial technology, regulatory compliance, and the need for data-driven decision-making. As global populations grow and urbanization accelerates, there is a growing need for efficient land resource management. Land management software offers tools to optimize land use, enhance productivity in agriculture, forestry, and urban planning, and ensure sustainable development practices.

Advancements in geospatial technology, such as Geographic Information Systems (GIS), remote sensing, and satellite imagery, have significantly enhanced the capabilities of land management software, enabling more accurate mapping, monitoring, and analysis of land resources. Regulatory compliance and environmental concerns also drive the adoption of land management software among government agencies, landowners, and businesses.

Data-driven decision-making is another driving factor, as land management software provides powerful analytical tools for processing large volumes of spatial data, generating insights, and supporting data-driven decision-making processes. The growing awareness of climate change risks and the need for resilient land management practices drives the adoption of software solutions that enable climate-smart land management.

Precision agriculture practices are increasingly emphasized in the agricultural sector, with land management software playing a critical role in supporting these practices. The emergence of integrated land management platforms that combine GIS, asset management, and workflow automation capabilities is also driving the adoption of comprehensive software solutions.

In conclusion, the growth of land management software is driven by the need for efficient land use, advancements in technology, regulatory requirements, and the recognition of the importance of sustainable land management practices in addressing global challenges such as food security, environmental degradation, and climate change.

Full description and workflow TBD. Contact the Parks GIS Team for more information in the interim via email: MCParksGIS@montgomeryparks.org.The Management Areas layer contains the subdivided areas of Montgomery Parks Operations, Northern Parks Division and Southern Parks Division, that are led by Park Managers and are formally known as management areas. The layer’s features contain the lead Park Manager’s full name and phone number as attributes. The layer is primarily used as a reference for the delineation of park management boundaries in static and live map products or as a data source for determining the management area of a feature via its location such as a park asset.Locations(s)SDE:Q:\Layer Files\GIS Database5.sde\PARKS.Parks\PARKS.ManagementAreas_PyQ:\Layer Files\GIS Database5.sde\PARKS.ManagementRegions_Py_VWQ:Q:\Layer Files\Parks – Management Boundaries.lyrServices:https://montgomeryplans.org/server/rest/services/Parks/ManagementBoundaries/MapServerhttps://montgomeryplans.org/server/rest/services/Parks/ManagementBoundaries/FeatureServer

Full description and workflow TBD. Contact the Parks GIS Team for more information in the interim via email: MCParksGIS@montgomeryparks.org.The Management Areas layer contains the subdivided areas of Montgomery Parks Operations, Northern Parks Division and Southern Parks Division, that are led by Park Managers and are formally known as management areas. The layer’s features contain the lead Park Manager’s full name and phone number as attributes. The layer is primarily used as a reference for the delineation of park management boundaries in static and live map products or as a data source for determining the management area of a feature via its location such as a park asset.Locations(s)SDE:Q:\Layer Files\GIS Database5.sde\PARKS.Parks\PARKS.ManagementAreas_PyQ:\Layer Files\GIS Database5.sde\PARKS.ManagementRegions_Py_VWQ:Q:\Layer Files\Parks – Management Boundaries.lyrServices:https://montgomeryplans.org/server/rest/services/Parks/ManagementBoundaries/MapServerhttps://montgomeryplans.org/server/rest/services/Parks/ManagementBoundaries/FeatureServer